Contingent capital (coco) automatically recapitalizes the banking system during financial crises if the trigger mechanism is properly designed. We propose a dual trigger mechanism based on: (1) aggregate systemic risk in the banking system, measured using CATFIN, and (2) the individual bank’s contribution to overall systemic risk, measured using delta CoVaR. The dual trigger is highly correlated with system-wide insolvency risk and prices systemic risk. We set different triggers for banks, insurance companies and broker-dealers. Using the 99% cut-off, systemic coco issued by Lehman and Bear Stearns would have been triggered in November 2007, months prior to their actual demise.

[We derive a measure of aggregate systemic risk, designated CATFIN, that complements bank-specific systemic risk measures by forecasting macroeconomic downturns six months into the future using out-of-sample tests conducted with U.S., European, and Asian bank data. Consistent with bank "specialness," the CATFIN of both large and small banks forecasts macroeconomic declines, whereas a similarly defined measure for both nonfinancial firms and simulated "fake banks" has no marginal predictive ability. High levels of systemic risk in the banking sector impact the macroeconomy through aggregate lending activity. A conditional asset pricing model shows that CATFIN is priced for financial and nonfinancial firms.]

We find that the stock market underreacts to stock-level liquidity shocks: liquidity shocks are not only positively associated with contemporaneous returns, but they also predict future return continuations for up to six months. Long-short portfolios sorted on liquidity shocks generate significant returns of 0.70% to 1.20% per month that are robust across alternative shock measures and after controlling for risk factors and stock characteristics. Furthermore, we show that investor inattention and illiquidity contribute to the underreaction: while both are significant in explaining short-term return predictability of liquidity shocks, the inattention-based mechanism is more powerful for the longer-term return predictability.

We derive a measure of aggregate systemic risk, designated CATFIN, that complements bank-specific systemic risk measures by forecasting macroeconomic downturns six months into the future using out-of-sample tests conducted with U.S., European, and Asian bank data. Consistent with bank “specialness,” the CATFIN of both large and small banks forecasts macroeconomic declines, whereas a similarly defined measure for both nonfinancial firms and simulated “fake banks” has no marginal predictive ability. High levels of systemic risk in the banking sector impact the macroeconomy through aggregate lending activity. A conditional asset pricing model shows that CATFIN is priced for financial and nonfinancial firms.

This paper investigates the role of high-order moments in the estimation of conditional value at risk (VaR). We use the skewed generalized t distribution (SGT) with time-varying parameters to provide an accurate characterization of the tails of the standardized return distribution. We allow the high-order moments of the SGT density to depend on the past information set, and hence relax the conventional assumption in conditional VaR calculation that the distribution of standardized returns is iid. The maximum likelihood estimates show that the time-varying conditional volatility, skewness, tail-thickness, and peakedness parameters of the SGT density are statistically significant. The in-sample and out-of-sample performance results indicate that the conditional SGT-GARCH approach with autoregressive conditional skewness and kurtosis provides very accurate and robust estimates of the actual VaR thresholds.

We investigate the role of economic uncertainty in the cross-sectional pricing of individual stocks and equity portfolios. We estimate stock exposure to an economic uncertainty index and show that stocks in the lowest uncertainty beta decile generate 6% more annualized risk-adjusted return compared to stocks in the highest uncertainty beta decile. We find that the uncertainty premium is driven by the outperformance (underperformance) by stocks with negative (positive) uncertainty beta. Our results indicate that uncertainty-averse investors demand extra compensation to hold stocks with negative uncertainty beta and they are willing to pay high prices for stocks with positive uncertainty beta.

We find that the stock market underreacts to stock-level liquidity shocks: liquidity shocks are not only positively associated with contemporaneous returns, but they also predict future return continuations for up to six months. Long-short portfolios sorted on liquidity shocks generate significant returns of 0.70% to 1.20% per month that are robust across alternative shock measures and after controlling for risk factors and stock characteristics. Furthermore, we show that investor inattention and illiquidity contribute to the underreaction: while both are significant in explaining short-term return predictability of liquidity shocks, the inattention-based mechanism is more powerful for the longer-term return predictability.

We document distinctly different clientele effects on investor attention and return responses to information. Macro news crowds out retail investor attention to firms’ earnings news by 49%. For stocks with high retail ownership, macro news dampens earnings announcement returns by 17% and substantially increases post-announcement drift, especially during high VIX periods. In contrast, macro news increases institutional investor attention to scheduled earnings announcements but not their attention to unscheduled analysts’ forecast revisions. The findings confirm the implications of rational inattention models and highlight the importance of considering clientele effects in understanding the effect of news on attention and asset prices.

The low (high) abnormal returns of stocks with high (low) beta, which we refer to as the beta anomaly, is one of the most persistent anomalies in empirical asset pricing research. This article demonstrates that investors’ demand for lottery-like stocks is an important driver of the beta anomaly. The beta anomaly is no longer detected when beta-sorted portfolios are neutralized to lottery demand, regression specifications control for lottery demand, or factor models include a lottery demand factor. The beta anomaly is concentrated in stocks with low levels of institutional ownership and it exists only when the price impact of lottery demand is concentrated in high-beta stocks.